4.4 Article

Beta Diversity Patterns Derived from Island Biogeography Theory

Journal

AMERICAN NATURALIST
Volume 194, Issue 3, Pages E52-E65

Publisher

UNIV CHICAGO PRESS
DOI: 10.1086/704181

Keywords

neutral theory; null model; metacommunity; incidence function; community assembly; regional species pool

Funding

  1. National Science Foundation (NSF) [DBI-1262600, DEB-1441737]
  2. National Aeronautics and Space Administration [80NSSC17K0282, 80NSSC18K0435]
  3. NSF [DEB-1754012]

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Metacommunity theory and its constituent theory of island biogeography (TIB) have the potential to unify ecology across different scales. The TIB has been successful in predicting alpha diversity patterns, such as species-area relationships and species-abundance distributions, but lags behind in predicting spatial beta diversity patterns. In this study we use island biogeography theory as the starting point to integrate spatial beta diversity patterns into metacommunity theory. We first derive theoretical predictions for the expected beta diversity patterns under the classic MacArthur and Wilson framework, where all species have equal colonization and extinction rates. We then test these predictions for the avian community composition of 42 islands (and 93 species) in Thousand Island Lake, China. Our theoretical results corroborate that longer distance and smaller area lead to higher beta diversity and further reveal that pairwise beta diversity is independent of the size of the mainland species pool. We also find that for the partitioned pairwise beta diversity components, the turnover component increases with the ratio of extinction rates and colonization rates, while the nestedness component is a unimodal function of the ratio of extinction rates and colonization rates. For the empirical island system, we find that beta diversity patterns better distinguish a species-equivalent model from a species-nonequivalent model than alpha diversity patterns. Our findings suggest that beta diversity patterns provide a powerful tool in detecting nonneutral processes, and our model has the potential to incorporate more biological realism in future analyses.

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